عنوان مقاله [English]
Considering the complexity of the wastewater treating processes, the use of physical-based models requires more time and cost. Therefore, in this study, black box artificial intelligence models (AI) including feed forward neural network (FFNN), adoptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and autoregressive integrated moving average linear model (ARIMA) were used to predict effluent biological oxygen demand (BODeff) and chemical oxygen demand (CODeff) of Tabriz wastewater treatment plant (WWTP) using the daily data collected from 2016 to 2018. The input data set included daily influent BOD, COD, total suspended solids (TSS), pH at the current time (t) and BODeff and CODeff at the previous time (t-1) and the output data included BODeff and CODeff at t. The results of the single models indicated that SVR model provides better results than the other single models. To improve the prediction of BODeff and CODeff parameters, the data post-processing ensemble method was also used. In the ensemble modeling, simple linear averaging, weighted linear averaging and neural network ensemble methods were applied to enhance the performance of the single AI models. The results indicated that using non-linear models gave better results than ARIMA linear model and SVR model gave the highest determination coefficient (DC) comparing other models. Also, the use of hybrid models, especially nonlinear ensemble models with artificial neural networks, increased the modeling performance up to 15% in the validation step.